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Record W4410335637 · doi:10.1088/2053-1591/add85d

Surface blackening phenomenon and formation mechanism in high-temperature annealed nickel-based superalloys

2025· article· en· W4410335637 on OpenAlex
Yan Yang, Zhicheng Cheng, Wei Yu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials Research Express · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsNickel Institute
Fundersnot available
KeywordsSuperalloyMaterials scienceMetallurgyNickelMechanism (biology)Annealing (glass)MicrostructurePhysics

Abstract

fetched live from OpenAlex

Abstract Owing to stringent service conditions, nickel-based superalloys must meet exceptionally high surface quality standards. After cold rolling, these alloys can blacken during annealing, affecting appearance and performance. The surface-element distribution of GH4169 alloy annealed under hydrogen protection was analyzed via XPS, and reaction free-energy changes were calculated using thermodynamic software. The blackened surface contained oxides and carbides, primarily Cr 2 O 3 , Cr(OH) 3 , TiO 2 , Nb 2 O 5 , NbC, and free carbon. Residual water vapor in the protective hydrogen gas and rolling-oil remnants generated C, CO 2 , CO, and H 2 O, which then reacted with the alloy during high-temperature annealing to form a blackened layer. The selective oxidation of Cr, Nb, and Ti, along with the inward diffusion of carbon, leads to the compositional differences observed in the blackened layer. Lowering the protective gas dew point, improving rolling oil, and effective degreasing eliminated blackening.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.270
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it